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首页> 外文期刊>Atmospheric environment >Assessment of impact of unaccounted emission on ambient concentration using DEHM and AERMOD in combination with WRF
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Assessment of impact of unaccounted emission on ambient concentration using DEHM and AERMOD in combination with WRF

机译：使用DEHM和AERMOD结合WRF评估未解释排放对环境浓度的影响

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The accuracy of the results from an air quality model is governed by the quality of emission and meteorological data inputs in most of the cases. In the present study, two air quality models were applied for inverse modelling to determine the particulate matter emission strengths of urban and regional sources in and around Mumbai in India. The study takes outset in an existing emission inventory for Total Suspended Particulate Matter (TSPM). Since it is known that the available TSPM inventory is uncertain and incomplete, this study will aim for qualifying this inventory through an inverse modelling exercise. For use as input to the air quality models in this study, onsite meteorological data has been generated using the Weather Research Forecasting (WRF) model. The regional background concentration from regional sources is transported in the atmosphere from outside of the study domain. The regional background concentrations of particulate matter were obtained from model calculations with the Danish Eulerian Hemisphere Model (DEHM) for regional sources. The regional background concentrations obtained from DEHM were then used as boundary concentrations in AERMOD calculations of the contribution from local urban sources. The results from the AERMOD calculations were subsequently compared with observed concentrations and emission correction factors obtained by best fit of the model results to the observed concentrations. The study showed that emissions had to be up-scaled by between 14 and 55% in order to fit the observed concentrations; this is of course when assuming that the DEHM model describes the background concentration level of the right magnitude. (C) 2016 Elsevier Ltd. All rights reserved.
机译：在大多数情况下，空气质量模型结果的准确性受排放质量和气象数据输入的支配。在本研究中，将两个空气质量模型用于逆向建模，以确定印度孟买及其周边地区的城市和区域源的颗粒物排放强度。该研究从总悬浮颗粒物（TSPM）的现有排放清单开始。由于已知可用的TSPM清单是不确定的和不完整的，因此本研究旨在通过反向建模练习对清单进行鉴定。为了用作本研究中的空气质量模型的输入，已使用天气研究预报（WRF）模型生成了现场气象数据。来自区域来源的区域背景浓度是从研究域外部在大气中传输的。从丹麦欧拉半球模型（DEHM）的模型计算中获得了区域性颗粒物的区域背景浓度。然后，从DEHM获得的区域背景浓度被用作AERMOD计算本地城市来源贡献的边界浓度。随后将AERMOD计算得出的结果与观察到的浓度和通过将模型结果与观察到的浓度最佳拟合而获得的排放校正因子进行比较。研究表明，排放量必须按比例增加14％至55％，才能符合所观察到的浓度。当然，这是在假设DEHM模型描述了正确幅度的背景浓度水平时进行的。 （C）2016 Elsevier Ltd.保留所有权利。

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